external/cc-skills-golang/golang-popular-libraries/SKILL.md
Recommends production-ready Golang libraries and frameworks. Apply when the user explicitly asks for library suggestions, wants to compare alternatives, needs to choose a library for a specific task, or when a new dependency is being added to the project.
npx skillsauth add seikaikyo/dash-skills golang-popular-librariesInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Persona: You are a Go ecosystem expert. You know the library landscape well enough to recommend the simplest production-ready option — and to tell the developer when the standard library is already enough.
When recommending libraries, prioritize:
Find more libraries here: https://github.com/avelino/awesome-go
This skill is not exhaustive. Please refer to library documentation and code examples for more information. When exploring a candidate library, → See samber/cc-skills-golang@golang-pkg-go-dev skill (godig) for docs, symbols, versions, importers, and known vulnerabilities — prefer it over Context7 for Go package facts. Once a candidate is added to your build, → See samber/cc-skills-golang@golang-gopls skill (gopls) to browse its actual resolved source and compare candidates side by side. Context7 remains a fallback for docs not indexed on pkg.go.dev.
When recommending libraries:
imported-by count on pkg.go.dev as a popularity and indirect quality signal — widely-imported libraries are more battle-tested and have stronger backward-compatibility pressure; → See samber/cc-skills-golang@golang-pkg-go-dev skill to count importers and compare alternativesRemember: The best library is often no library at all. Go's standard library is excellent and sufficient for many use cases.
samber/cc-skills-golang@golang-dependency-management skill for adding, auditing, and managing dependenciessamber/cc-skills-golang@golang-pkg-go-dev skill to vet a candidate library on pkg.go.dev — versions, importers, licenses, and known vulnerabilities — before adopting itsamber/cc-skills-golang@golang-samber-do skill for samber/do dependency injection detailssamber/cc-skills-golang@golang-samber-hot skill for samber/hot in-memory caching detailssamber/cc-skills-golang@golang-samber-oops skill for samber/oops error handling detailssamber/cc-skills-golang@golang-stretchr-testify skill for testify testing detailssamber/cc-skills-golang@golang-grpc skill for gRPC implementation detailstools
Conduct comprehensive GDPR compliance assessments by evaluating data processing activities against EU Regulation 2016/679, including Article 30 records of processing, lawful basis validation, data subject rights implementation, Data Protection Impact Assessments (DPIAs) under Article 35, breach notification procedures, international transfer safeguards (SCCs, adequacy decisions), and technical/organizational measures under Article 32. Use when processing personal data of EU residents, preparing for supervisory authority audits, implementing privacy-by-design for new systems, scoping compliance gaps for M&A due diligence, assessing third-party processors, or responding to data subject access requests at scale. Incorporates 2026 guidance from ICO, EDPB, and post-Data (Use and Access) Act 2025 UK-GDPR considerations. Do not use for implementing specific Article 32 controls — use implementing-gdpr-data-protection-controls; or for DSAR automation — use implementing-gdpr-data-subject-access-request.
tools
Parse Windows forensic artifacts—$MFT/$J (MFTECmd), Prefetch (PECmd), registry hives (RECmd), shellbags, and Amcache—into normalized CSV/JSON with Eric Zimmerman's EZ Tools, then load results into Timeline Explorer for analysis. Use during DFIR/incident-response investigations, after triage collection (e.g. with KAPE), to establish program execution, file/folder access, and persistence evidence from acquired forensic images.
development
Build automated multi-turn adversarial attacks against conversational LLM targets using Microsoft PyRIT's RedTeamingOrchestrator, CrescendoOrchestrator (gradual escalation), and TreeOfAttacksWithPruningOrchestrator (adaptive branching), with scorer feedback loops and persisted conversation memory. Use when single-shot LLM scanning is insufficient and you need multi-turn, scorer-driven AI red-team campaigns against a chatbot or agent.
testing
Stand up MISP, enable and cache curated threat feeds (CIRCL, abuse.ch, Feodo Tracker), apply warninglists to suppress false positives, query indicators with PyMISP, and export attributes as auto-generated Suricata/Sigma/Wazuh detection rules. Use when maturing a MISP instance to actively drive detection, curating threat feeds with quality controls, or automating IOC-to-detection pipelines for the SIEM/IDS.